RAG implementation cost
A RAG pilot costs $5,000 to $10,000. Production is scoped from the pilot.
A RAG pilot costs $5,000 to $10,000 with us: retrieval-augmented search or chat over one document set, answers with citations, and an evaluation set that scores retrieval and answers against your own questions. A full production system is scoped after a call, using what the pilot measured. RAG is our specialty, and we run it in our own products.
Updated · Prices in USD · Acasa Labs
The numbers
What RAG costs with us.
| What you get | Price (USD) | Timeline | What it covers |
|---|---|---|---|
| RAG pilot over one document set | Price (USD)$5,000 to $10,000 | TimelineAgreed in the scope | Search or chat over one document set, answers with sources, and an evaluation set that measures whether the answers are right. |
| Production RAG system | Price (USD)Scoped after a call | TimelineScoped after a call | More sources, scheduled ingestion, permissions on retrieval, hybrid search and reranking where needed, monitoring, and cost tracking. |
| Dedicated developer | Price (USD)$1,500 to $2,000 a month | TimelineMonthly, about 160 hours | One developer full time on your roadmap, in your repository, with a senior reviewing pull requests. |
| Maintenance and support | Price (USD)$100 to $300 a month | TimelineMonthly | Updates, fixes, monitoring, and the small changes that come up after launch. |
We do not quote a production RAG system up front because its cost depends on things only the pilot reveals: how the documents parse, what retrieval needs, and what score is good enough for your use.
Every price on this page is our own rate card, in US dollars. The minimum project is $1,500. A dedicated developer is $1,500 to $2,000 a month. Hire engagements start with a risk-free first week, and you own the code, repositories, and cloud accounts from day one. Indian clients are invoiced in INR by Acasa Labs (OPC) Private Limited. See every engagement model and rate.
01
What drives the cost of RAG
In RAG, most of the work is not the model. It is getting the right passages in front of it, every time, and measuring that you do.
Ingestion
Parsing PDFs, scans, slides, spreadsheets, web pages, and audio into clean text with structure kept. Tables and scanned pages are the expensive cases.
Retrieval quality
Chunking, embeddings, filters, and sometimes hybrid search and reranking. Each step that raises the evaluation score adds work, and the pilot shows which ones you need.
Evaluation
A question set with known answers, and scoring for both retrieval and the final answer. Without it, nobody can say whether a change made the system better.
Permissions and freshness
Documents that change daily need scheduled ingestion. Documents only some users may see need access rules inside retrieval.
Scale and the vector database
Moderate volumes fit well in Postgres with pgvector. Millions of chunks, or heavy filtering, may call for a dedicated vector database such as Qdrant.
02
What the pilot gives you
At the end of the pilot you have a working RAG system over one document set, a measured score on your own questions, and a written list of what production would need. That list is what we price the production system from.
The pilot is not a throwaway demo. Ingestion, the index, and the evaluation set are the same pieces a production system needs, and the evaluation set keeps being useful for every change after launch.
03
Running costs
Embedding, model calls, and the vector database have running costs, billed by those providers to accounts in your name. They are not part of our price. We build in token and cost logging per call so you can see the cost per answer.
Example builds
RAG and retrieval we have built.
These are from our own products and a hackathon case study. They show the retrieval work we have done, not what a pilot would include.
RAG over every source a student uploads
NoteTubeVideos, PDFs, DOCX, images, articles, and audio are parsed, chunked, embedded, and stored in Qdrant with per-user and per-session scoping. Answers stream back grounded in that material, not the open web.
Page-targeted retrieval with citations
SigmaCPMSigmaCPM finds candidate pages in a tender package before extraction, so the model reads the right pages. Every extracted value must carry a quote found on the cited page.
Vector search beyond text
Acasa SentinelAcasa Sentinel stores a 768-dimension DINOv2 embedding for every vehicle sighting in Postgres with pgvector, so a vehicle can be found across cameras when its plate is unreadable.
04
How to spend less on RAG
- 01Pilot one document set with one group of users before connecting everything.
- 02Start on Postgres with pgvector if your volume allows it. One fewer system to run.
- 03Remove outdated and duplicate documents before ingestion.
- 04Write the evaluation questions with the people who know the answers.
- 05Add reranking and hybrid search only when the evaluation score says you need them.
05
When a cheaper option is the better choice
If your documents are a small set of public, well-written pages, a built-in AI search in your help desk or site platform may be enough, with no build.
If people mostly need to find a document rather than get an answer, plain keyword search with good filters can beat RAG for a fraction of the cost.
And if your documents rarely change and fit within a model's context window, sending them directly to a model can work without a retrieval system at all. RAG earns its cost when the documents are large, private, changing, or when every answer must cite its source.
Rough number first
Pick platforms, features, and integrations, and get a range and a timeline in a minute.
Questions
RAG cost, asked often.
How much does RAG implementation cost?
With us, a pilot over one document set costs $5,000 to $10,000, including an evaluation set. Production is scoped after a call, based on the pilot.
Why not quote production up front?
Because the cost depends on how your documents parse and what retrieval needs to hit a good score. A pilot finds that out for a fixed price.
Is RAG cheaper than fine-tuning?
For answering from your own documents, usually yes, and it can cite sources and stay current. We compare the two in our RAG vs fine-tuning guide.
Which vector database do you use?
Postgres with pgvector when volume allows, and Qdrant when it does not. NoteTube runs on Qdrant; Acasa Sentinel runs on pgvector.
Are model and database costs included?
No. They are billed by the providers to accounts in your name, and we track cost per answer.
Who owns the system?
You do: the code, the index, the evaluation set, and every account.
How do we know the pilot worked?
By the evaluation set. Before the pilot starts, we agree the questions and what a good score is. At the end you see the score, the questions it got wrong, and why.
RAG cost
Get a fixed estimate for your RAG pilot.
No call needed to start. Tell us what the documents are and what people need to ask them, and we reply with a scope and a number.